Optimizing inputs for LLMs ensures better, more consistent outputs while leveraging the full potential of the model's underlying capabilities. By understanding core concepts like tokenization, embeddings, self-attention, and context limits, you can tailor inputs to achieve desired outcomes reliably. Below, you'll find fundamental techniques and best practices organized into practical strategies.
🧠 Theory: LLMs always follow probabilities when generating text. For every token, the model calculates a probability distribution based on the context provided. By carefully structuring inputs or presenting examples, we can shift the probabilities toward the desired outcome: